Extragalactic diffuse gamma-rays from dark matter annihilation: revised prediction and full modelling uncertainties

TL;DR

This paper revises the predicted extragalactic gamma-ray intensity from dark matter annihilation, accounting for modeling uncertainties, and finds the intensity is lower than previous estimates, affecting dark matter constraints.

Contribution

It provides a comprehensive update on the gamma-ray intensity prediction from dark matter annihilation, including detailed uncertainty analysis and implications for observational limits.

Findings

Fiducial intensity is five times lower than previous estimates.

Including smaller halos increases the predicted intensity by a factor of five.

Self-interacting dark matter reduces gamma-ray emission by a factor of three.

Abstract

Recent high-energy data from Fermi-LAT on the diffuse gamma-ray background have been used to set among the best constraints on annihilating TeV cold dark matter candidates. In order to assess the robustness of these limits, we revisit and update the calculation of the isotropic extragalactic gamma-ray intensity from dark matter annihilation. The emission from halos with masses $\geq10^{10}\,M_{\odot}$ provides a robust lower bound on the predicted intensity. The intensity including smaller halos whose properties are extrapolated from their higher mass counterparts is typically 5 times higher, and boost from subhalos yields an additional factor ~1.5. We also rank the uncertainties from all ingredients and provide a detailed error budget for them. Overall, our fiducial intensity is a factor 5 lower than the one derived by the Fermi-LAT collaboration in their latest analysis. This indicates that the limits set on extragalactic dark matter annihilations could be relaxed by the same factor. We also calculate the expected intensity for self-interacting dark matter in massive halos and find the emission reduced by a factor 3 compared to the collisionless counterpart. The next release of the CLUMPY code will provide all the tools necessary to reproduce and ease future improvements of this prediction.